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<a href="_logistic_regression_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Logistic Regression</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      O.Krause</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2017</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> *</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> *</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> *</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_TRAINERS_LOGISTICREGRESSION_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_ALGORITHMS_TRAINERS_LOGISTICREGRESSION_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_linear_model_8h.html">shark/Models/LinearModel.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_weighted_trainer_8h.html">shark/Algorithms/Trainers/AbstractWeightedTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"></span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \brief Trainer for Logistic regression</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// Logistic regression solves the following optimization problem:</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \f[ \min_{w,b} \sum_i u_i l(y_i,f(x_i^Tw+b)) +\lambda_1 |w|_1 +\lambda_2 |w|^2_2 \f]</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// Where \f$l\f$ is the cross-entropy loss and \f$u_i\f$ are individual weuights for each point(assumed to be 1).</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// Logistic regression is one of the most well known</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// machine learning algorithms for classification using linear models.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// The solver is based on LBFGS for the case where no l1-regularization is used. Otherwise</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// the problem is transformed into a constrained problem and the constrined-LBFGS algorithm</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// is used. This is one of the most efficient solvers for logistic regression as long as the</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// number of data points is not too large.</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputVectorType = RealVector&gt;</div>
<div class="foldopen" id="foldopen00059" data-start="{" data-end="};">
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html">   59</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_logistic_regression.html" title="Trainer for Logistic regression.">LogisticRegression</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer</a>&lt;LinearClassifier&lt;InputVectorType&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;&gt;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>{</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer&lt;LinearClassifier&lt;InputVectorType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">base_type</a>;</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a1d653feba69db50f94611dfba14f9d88">   64</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_weighted_trainer.html#ad2ad0a52ecd9ac8677df6dbf403b68b4">base_type::ModelType</a> <a class="code hl_typedef" href="classshark_1_1_logistic_regression.html#a1d653feba69db50f94611dfba14f9d88">ModelType</a>;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#ae4a9dfa85dc2e843c49e77d567a9a76d">   65</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_class" href="classshark_1_1_labeled_data.html">base_type::DatasetType</a> <a class="code hl_typedef" href="classshark_1_1_logistic_regression.html#ae4a9dfa85dc2e843c49e77d567a9a76d">DatasetType</a>;</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#afec0e13e21ec6fd4b291a660f75c44b3">   66</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_class" href="classshark_1_1_weighted_labeled_data.html" title="Weighted data set for supervised learning.">base_type::WeightedDatasetType</a> <a class="code hl_typedef" href="classshark_1_1_logistic_regression.html#afec0e13e21ec6fd4b291a660f75c44b3">WeightedDatasetType</a>;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment"></span> </div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">    /// \brief Constructor.</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">    /// \param  lambda1    value of the 1-norm regularization parameter (see class description)</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">    /// \param  lambda2    value of the 2-norm regularization parameter (see class description)</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">    /// \param  bias          whether to train with bias or not</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">    /// \param  accuracy  stopping criterion for the iterative solver, maximal gradient component of the objective function (see class description)</span></div>
<div class="foldopen" id="foldopen00074" data-start="{" data-end="}">
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a825243d1d1a808be12d822333c58f6fd">   74</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a825243d1d1a808be12d822333c58f6fd" title="Constructor.">LogisticRegression</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a6608d0494e8d9cc32d6a1a14a9ddbd90" title="Return the current setting of the l1-regularization parameter.">lambda1</a> = 0, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ab803c9232597463aa686b02434d3a15f" title="Return the current setting of the l2-regularization parameter.">lambda2</a> = 0, <span class="keywordtype">bool</span> bias = <span class="keyword">true</span>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a> = 1.e-8)</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    : m_bias(bias){</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ad0cbcad123fdab3818c063388d4bd458" title="Set the l1-regularization parameter.">setLambda1</a>(<a class="code hl_function" href="classshark_1_1_logistic_regression.html#a6608d0494e8d9cc32d6a1a14a9ddbd90" title="Return the current setting of the l1-regularization parameter.">lambda1</a>);</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a9c836fe21f4db962f7f50350ad90e4e0" title="Set the l2-regularization parameter.">setLambda2</a>(<a class="code hl_function" href="classshark_1_1_logistic_regression.html#ab803c9232597463aa686b02434d3a15f" title="Return the current setting of the l2-regularization parameter.">lambda2</a>);</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a5e792090a1db8d5eb4503e10bba38872" title="Set the accuracy (maximal gradient component of the optimization problem).">setAccuracy</a>(<a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>);</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    }</div>
</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment"></span> </div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00082" data-start="{" data-end="}">
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a8513a39378376eb2155e52fd1ab504f4">   82</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a8513a39378376eb2155e52fd1ab504f4" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;LogisticRegression&quot;</span>; }</div>
</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span> </div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment"></span> </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">    /// \brief Return the current setting of the l1-regularization parameter.</span></div>
<div class="foldopen" id="foldopen00087" data-start="{" data-end="}">
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a6608d0494e8d9cc32d6a1a14a9ddbd90">   87</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a6608d0494e8d9cc32d6a1a14a9ddbd90" title="Return the current setting of the l1-regularization parameter.">lambda1</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keywordflow">return</span> m_lambda1;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    }</div>
</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    <span class="comment"></span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">    /// \brief Return the current setting of the l2-regularization parameter.</span></div>
<div class="foldopen" id="foldopen00092" data-start="{" data-end="}">
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#ab803c9232597463aa686b02434d3a15f">   92</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ab803c9232597463aa686b02434d3a15f" title="Return the current setting of the l2-regularization parameter.">lambda2</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <span class="keywordflow">return</span> m_lambda2;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    }</div>
</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment"></span> </div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    /// \brief Set the l1-regularization parameter.</span></div>
<div class="foldopen" id="foldopen00097" data-start="{" data-end="}">
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#ad0cbcad123fdab3818c063388d4bd458">   97</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ad0cbcad123fdab3818c063388d4bd458" title="Set the l1-regularization parameter.">setLambda1</a>(<span class="keywordtype">double</span> lambda){</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(lambda &gt;= 0.0, <span class="stringliteral">&quot;Lambda1 must be positive&quot;</span>);</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        m_lambda1 = lambda;</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    }</div>
</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment"></span> </div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// \brief Set the l2-regularization parameter.</span></div>
<div class="foldopen" id="foldopen00103" data-start="{" data-end="}">
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a9c836fe21f4db962f7f50350ad90e4e0">  103</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a9c836fe21f4db962f7f50350ad90e4e0" title="Set the l2-regularization parameter.">setLambda2</a>(<span class="keywordtype">double</span> lambda){</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(lambda &gt;= 0.0, <span class="stringliteral">&quot;Lambda2 must be positive&quot;</span>);</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        m_lambda2 = lambda;</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// \brief Return the current setting of the accuracy (maximal gradient component of the optimization problem).</span></div>
<div class="foldopen" id="foldopen00108" data-start="{" data-end="}">
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36">  108</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <span class="keywordflow">return</span> m_accuracy;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    }</div>
</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"></span> </div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// \brief Set the accuracy (maximal gradient component of the optimization problem).</span></div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a5e792090a1db8d5eb4503e10bba38872">  113</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a5e792090a1db8d5eb4503e10bba38872" title="Set the accuracy (maximal gradient component of the optimization problem).">setAccuracy</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>){</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a> &gt; 0.0, <span class="stringliteral">&quot;Accuracy must be positive&quot;</span>);</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        m_accuracy = <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a08948cf00fbe0ae3263449ca3f49ab36" title="Return the current setting of the accuracy (maximal gradient component of the optimization problem).">accuracy</a>;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    }</div>
</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment"></span> </div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// \brief Get the regularization parameters lambda1 and lambda2 through the IParameterizable interface.</span></div>
<div class="foldopen" id="foldopen00119" data-start="{" data-end="}">
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a45c601e09b06f3b464fdc978f9d40493">  119</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a45c601e09b06f3b464fdc978f9d40493" title="Get the regularization parameters lambda1 and lambda2 through the IParameterizable interface.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="keywordflow">return</span> {m_lambda1,m_lambda2};</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    }</div>
</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment"></span> </div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    /// \brief Set the regularization parameters lambda1 and lambda2 through the IParameterizable interface.</span></div>
<div class="foldopen" id="foldopen00124" data-start="{" data-end="}">
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a99b7a94494a7544b82164c70152f7434">  124</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a99b7a94494a7544b82164c70152f7434" title="Set the regularization parameters lambda1 and lambda2 through the IParameterizable interface.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; param){</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(param.size() == 2);</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ad0cbcad123fdab3818c063388d4bd458" title="Set the l1-regularization parameter.">setLambda1</a>(param(0));</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a9c836fe21f4db962f7f50350ad90e4e0" title="Set the l2-regularization parameter.">setLambda2</a>(param(1));</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    }</div>
</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"></span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// \brief Return the number of parameters (one in this case).</span></div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a119f9feb4a345c46e04fd1aad0875cd1">  131</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a119f9feb4a345c46e04fd1aad0875cd1" title="Return the number of parameters (one in this case).">numberOfParameters</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="keywordflow">return</span> 2;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    }</div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment"></span> </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">    /// \brief Train a linear model with logistic regression.</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#ad7301dbc776c3f6027c95817439dbd66">  136</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#ad7301dbc776c3f6027c95817439dbd66" title="Train a linear model with logistic regression.">train</a>(<a class="code hl_typedef" href="classshark_1_1_logistic_regression.html#a1d653feba69db50f94611dfba14f9d88">ModelType</a>&amp; model, <a class="code hl_class" href="classshark_1_1_labeled_data.html">DatasetType</a> <span class="keyword">const</span>&amp; dataset);</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    <span class="comment"></span></div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    /// \brief Train a linear model with logistic regression using weights.</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno"><a class="line" href="classshark_1_1_logistic_regression.html#a9e8dd39f9b5bf73301079f9d441a2b53">  139</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_logistic_regression.html#a9e8dd39f9b5bf73301079f9d441a2b53" title="Train a linear model with logistic regression using weights.">train</a>(<a class="code hl_typedef" href="classshark_1_1_logistic_regression.html#a1d653feba69db50f94611dfba14f9d88">ModelType</a>&amp; model, <a class="code hl_class" href="classshark_1_1_weighted_labeled_data.html" title="Weighted data set for supervised learning.">WeightedDatasetType</a> <span class="keyword">const</span>&amp; dataset);</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    <span class="keywordtype">bool</span> m_bias; <span class="comment">///&lt; whether to train with the bias parameter or not</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>    <span class="keywordtype">double</span> m_lambda1;             <span class="comment">///&lt; l1-regularization parameter</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    <span class="keywordtype">double</span> m_lambda2;             <span class="comment">///&lt; l2-regularization parameter</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    <span class="keywordtype">double</span> m_accuracy;           <span class="comment">///&lt; gradient accuracy</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>};</div>
</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span> </div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="comment">//reference to explicit external template instantiation</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_logistic_regression.html" title="Trainer for Logistic regression.">LogisticRegression&lt;RealVector&gt;</a>;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_logistic_regression.html" title="Trainer for Logistic regression.">LogisticRegression&lt;FloatVector&gt;</a>;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_logistic_regression.html" title="Trainer for Logistic regression.">LogisticRegression&lt;CompressedRealVector&gt;</a>;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_logistic_regression.html" title="Trainer for Logistic regression.">LogisticRegression&lt;CompressedFloatVector&gt;</a>;</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span> </div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>}</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span><span class="preprocessor">#endif</span></div>
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